DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_3:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
import os
os.chdir("F:/f/MY____/AAIC/AssignmentS/DonorsChoose_Data")
os.getcwd()
Out[1]:
In [2]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [3]:
from tqdm import tqdm
for i in tqdm(range(int(10e6))):
pass
In [4]:
project_data = pd.read_csv('train_data.csv', nrows = 30000)
resource_data = pd.read_csv('resources.csv')
In [5]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
In [6]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[6]:
1.2 preprocessing of project_subject_categories¶
In [7]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
1.3 preprocessing of project_subject_subcategories¶
In [8]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
1.3 Text preprocessing¶
In [9]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [10]:
project_data.head(2)
Out[10]:
In [11]:
# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
Introducing New Features¶
Consider these set of features for Set 5 in Assignment:
categorical dataschool_state clean_categories....clean_subcategories....project_grade_category....teacher_prefix
numerical data quantity....teacher_number_of_previously_posted_projects....price
New Features
sentiment score's of each of the essay : numerical data
number of words in the title : numerical data
number of words in the combine essays : numerical data
In [12]:
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [13]:
new_title = []
for i in tqdm(project_data['project_title']):
j = decontracted(i)
new_title.append(j)
In [14]:
#Introducing New Features
title_word_count = []
#for i in project_data['project_title']:
for i in tqdm(new_title):
j = len(i.split())
title_word_count.append(j)
#print(j)
project_data['title_word_count'] = title_word_count
In [15]:
project_data.head(2)
Out[15]:
In [16]:
new_essay = []
for i in tqdm(project_data['essay']):
j = decontracted(i)
new_essay.append(j)
In [17]:
essay_word_count = []
for i in tqdm(new_essay):
j = len(i.split())
essay_word_count.append(j)
#print(j)
project_data['essay_word_count'] = essay_word_count
In [18]:
project_data.head(2)
Out[18]:
Computing Sentiment Scores¶
In [19]:
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# import nltk
#nltk.download('vader_lexicon')
sid = SentimentIntensityAnalyzer()
for_sentiment = 'a person is a person no matter how small dr seuss i teach the smallest students with the biggest enthusiasm'
ss = sid.polarity_scores(for_sentiment)
for k in ss:
print('{0}: {1}, '.format(k, ss[k]), end='')
# we can use these 4 things as features/attributes (neg, neu, pos, compound)
# neg: 0.0, neu: 0.753, pos: 0.247, compound: 0.93
In [20]:
SID = SentimentIntensityAnalyzer()
#There is NEGITIVE and POSITIVE and NEUTRAL and COMPUND SCORES
#http://www.nltk.org/howto/sentiment.html
negitive = []
positive = []
neutral = []
compound = []
for i in tqdm(project_data['essay']):
j = SID.polarity_scores(i)['neg']
k = SID.polarity_scores(i)['neu']
l = SID.polarity_scores(i)['pos']
m = SID.polarity_scores(i)['compound']
negitive.append(j)
positive.append(k)
neutral.append(l)
compound.append(m)
In [21]:
project_data['negitive'] = negitive
project_data['positive'] = positive
project_data['neutral'] = neutral
project_data['compound'] = compound
In [22]:
project_data.head(2)
Out[22]:
Preparing Data for Models¶
In [23]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [24]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
In [25]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [26]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [27]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
Project_Essays preprocessing¶
In [28]:
#train_preprocessed_essays
# Combining all the above stundents
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_essays.append(sent.lower().strip())
In [29]:
preprocessed_essays[10]
Out[29]:
Project_Titles_preprocessing¶
In [30]:
# train_preprocessed_title
from tqdm import tqdm
preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_titles.append(sent.lower().strip())
In [31]:
preprocessed_titles[10]
Out[31]:
In [32]:
project_data.columns
Out[32]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data (optinal)
- quantity : numerical (optinal)
- teacher_number_of_previously_posted_projects : numerical
- price : numericalIn [33]:
project_data.drop(['essay'], axis=1, inplace=True)
project_data.drop(['project_title'], axis=1, inplace=True)
In [34]:
project_data['clean_essay'] = preprocessed_essays
project_data['clean_project_title'] = preprocessed_titles
project_data.head(1)
Out[34]:
In [35]:
project_data = project_data.fillna(project_data['teacher_prefix'].value_counts().index[0])
In [36]:
#Train Test Split
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(project_data, project_data["project_is_approved"],
test_size = 0.33, stratify = project_data["project_is_approved"],
random_state = 42)
#Train CV Split
#x_train, x_cv, y_train, y_cv = train_test_split(x_train, y_train, test_size = 0.33, stratify = y_train,
# random_state = 42)
In [37]:
print(x_test.columns)
print(x_train.columns)
1.5.1 Vectorizing Categorical data Using Response Coding¶
In [38]:
def Responsetable(table, col) :
cat = table[col].unique()
freq_Pos = []
for i in cat :
freq_Pos.append(len(table.loc[(table[col] == i) & (table['project_is_approved'] == 1)]))
freq_Neg = []
for i in cat :
freq_Neg.append(len(table.loc[(table[col] == i) & (table['project_is_approved'] == 0)]))
encoded_Pos = []
for i in range(len(cat)) :
encoded_Pos.append(freq_Pos[i]/(freq_Pos[i] + freq_Neg[i]))
encoded_Neg = []
encoded_Neg[:] = [1 - x for x in encoded_Pos]
encoded_Pos_val = dict(zip(cat, encoded_Pos))
encoded_Neg_val = dict(zip(cat, encoded_Neg))
return encoded_Pos_val, encoded_Neg_val
In [39]:
def Responsecode(table) :
pos_cleancat, neg_cleancat = Responsetable(table,'clean_categories')
pos_cleansubcat, neg_cleansubcat = Responsetable(table,'clean_subcategories')
pos_schoolstate, neg_schoolstate = Responsetable(table, 'school_state')
pos_teacherprefix, neg_teacherprefix = Responsetable(table, 'teacher_prefix')
pos_projgradecat, neg_projgradecat = Responsetable(table, 'project_grade_category')
df = pd.DataFrame()
df['clean_cat_pos'] = table['clean_categories'].map(pos_cleancat)
df['clean_cat_neg'] = table['clean_categories'].map(neg_cleancat)
df['clean_subcat_pos'] = table['clean_subcategories'].map(pos_cleansubcat)
df['clean_subcat_neg'] = table['clean_subcategories'].map(neg_cleansubcat)
df['school_state_pos'] = table['school_state'].map(pos_schoolstate)
df['school_state_neg'] = table['school_state'].map(neg_schoolstate)
df['teacher_prefix_pos'] = table['teacher_prefix'].map(pos_teacherprefix)
df['teacher_prefix_neg'] = table['teacher_prefix'].map(neg_teacherprefix)
df['proj_grade_cat_pos'] = table['project_grade_category'].map(pos_projgradecat)
df['proj_grade_cat_neg'] = table['project_grade_category'].map(neg_projgradecat)
return df
In [40]:
newTrain = Responsecode(x_train)
newTest = Responsecode(x_test)
In [41]:
def mergeEncoding(table, p, n) :
lstPos = table[p].values.tolist()
lstNeg = table[n].values.tolist()
frame = pd.DataFrame(list(zip(lstNeg, lstPos)))
return frame
In [42]:
#Clean Categories
X_train_clean_cat_ohe = mergeEncoding(newTrain, 'clean_cat_pos', 'clean_cat_neg')
X_test_clean_cat_ohe = mergeEncoding(newTest, 'clean_cat_pos', 'clean_cat_neg')
In [43]:
print(X_train_clean_cat_ohe.shape)
X_test_clean_cat_ohe.shape
Out[43]:
In [44]:
#Clean SUB Categories
X_train_clean_subcat_ohe = mergeEncoding(newTrain, 'clean_subcat_pos', 'clean_subcat_neg')
X_test_clean_subcat_ohe = mergeEncoding(newTest, 'clean_subcat_pos', 'clean_subcat_neg')
print(X_train_clean_subcat_ohe.shape)
print(X_test_clean_subcat_ohe.shape)
In [45]:
#Project Grade Category
X_train_grade_ohe = mergeEncoding(newTrain, 'proj_grade_cat_pos', 'proj_grade_cat_neg')
X_test_grade_ohe = mergeEncoding(newTest, 'proj_grade_cat_pos', 'proj_grade_cat_neg')
print(X_train_grade_ohe.shape)
print(X_test_grade_ohe.shape)
In [46]:
#School State
X_train_state_ohe = mergeEncoding(newTrain, 'school_state_pos', 'school_state_neg')
X_test_state_ohe = mergeEncoding(newTest, 'school_state_pos', 'school_state_neg')
print(X_train_state_ohe.shape)
print(X_test_state_ohe.shape)
In [47]:
#Teacher Prefix
X_train_teacher_ohe = mergeEncoding(newTrain, 'teacher_prefix_pos', 'teacher_prefix_neg')
X_test_teacher_ohe = mergeEncoding(newTest, 'teacher_prefix_pos', 'teacher_prefix_neg')
print(X_train_teacher_ohe.shape)
print(X_test_teacher_ohe.shape)
1.5.2 Vectorizing Text data¶
1.5.2.1 Bag of words¶
Minimum frequency of words 10 (min_df = 10) of ALL features
In [48]:
from sklearn.feature_extraction.text import CountVectorizer
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
vectorizer.fit(x_train['clean_essay'])
train_text_bow = vectorizer.fit_transform(x_train['clean_essay'])
print("Shape of matrix after one hot encodig ",train_text_bow.shape, y_train.shape)
In [49]:
#Vectorizing Test Data
# you can vectorize the title also
# before you vectorize the title make sure you preprocess it
test_text_bow = vectorizer.transform(x_test['clean_essay'])
print("Shape of matrix after one hot encodig ",test_text_bow.shape, y_test.shape)
Project_Title BOW¶
In [50]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
vectorizer.fit(x_train['clean_project_title'])
train_title_bow = vectorizer.fit_transform(x_train['clean_project_title'])
print("Shape of matrix after one hot encodig ",train_title_bow.shape)
In [51]:
#Vectorizing Test Data
test_title_bow = vectorizer.transform(x_test['clean_project_title'])
print("Shape of matrix after one hot encodig ",test_title_bow.shape)
1.5.2.2 TFIDF vectorizer¶
Here also we are considering word frequency of 10 words(min_df = 10) of all features
In [52]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(x_train['clean_essay'])
train_text_tfidf = vectorizer.fit_transform(x_train['clean_essay'])
print("Shape of matrix after one hot encodig ",train_text_tfidf.shape)
In [53]:
test_text_tfidf = vectorizer.transform(x_test['clean_essay'])
print("Shape of matrix after one hot encodig ",test_text_tfidf.shape)
Project Titles¶
In [54]:
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(x_train['clean_project_title'])
train_title_tfidf = vectorizer.fit_transform(x_train['clean_project_title'])
print("Shape of matrix after one hot encodig ",train_title_tfidf.shape)
In [55]:
test_title_tfidf = vectorizer.transform(x_test['clean_project_title'])
print("Shape of matrix after one hot encodig ",test_title_tfidf.shape)
1.5.2.3 Using Pretrained Models: Avg W2V¶
In [56]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
f = open(gloveFile,'r', encoding="utf8")
model = {}
for line in tqdm(f):
splitLine = line.split()
word = splitLine[0]
embedding = np.array([float(val) for val in splitLine[1:]])
model[word] = embedding
print ("Done.",len(model)," words loaded!")
return model
model = loadGloveModel('glove.42B.300d.txt')
# ============================
Output:
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495 words loaded!
# ============================
words = []
for i in preproced_texts:
words.extend(i.split(' '))
for i in preproced_titles:
words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))
inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
words_courpus = {}
words_glove = set(model.keys())
for i in words:
if i in words_glove:
words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
import pickle
with open('glove_vectors', 'wb') as f:
pickle.dump(words_courpus, f)
'''
Out[56]:
In [57]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
model = pickle.load(f)
glove_words = set(model.keys())
In [58]:
# average Word2Vec
# compute average word2vec for each review.
train_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_train['clean_essay']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_avg_w2v_vectors.append(vector)
print(len(train_avg_w2v_vectors))
print(len(train_avg_w2v_vectors[0]))
In [59]:
# average Word2Vec
# compute average word2vec for each review.
test_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_test['clean_essay']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_avg_w2v_vectors.append(vector)
print(len(test_avg_w2v_vectors))
print(len(test_avg_w2v_vectors[0]))
AVG_W2V Project_Titles¶
In [60]:
# average Word2Vec
# compute average word2vec for each review.
train_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_train['clean_project_title']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_title_avg_w2v_vectors.append(vector)
print(len(train_title_avg_w2v_vectors))
print(len(train_title_avg_w2v_vectors[0]))
In [61]:
# average Word2Vec
# compute average word2vec for each review.
test_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_test['clean_project_title']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_title_avg_w2v_vectors.append(vector)
print(len(test_title_avg_w2v_vectors))
print(len(test_title_avg_w2v_vectors[0]))
1.5.2.3 Using Pretrained Models: TFIDF weighted W2V¶
In [62]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(x_train['clean_essay'])
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [63]:
# average Word2Vec
# compute average word2vec for each review.
train_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_train['clean_essay']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_essay_tfidf_w2v_vectors.append(vector)
print(len(train_essay_tfidf_w2v_vectors))
print(len(train_essay_tfidf_w2v_vectors[0]))
In [64]:
# Similarly you can vectorize for title also
# average Word2Vec
# compute average word2vec for each review.
test_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_test['clean_essay']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_essay_tfidf_w2v_vectors.append(vector)
print(len(test_essay_tfidf_w2v_vectors))
print(len(test_essay_tfidf_w2v_vectors[0]))
Proect_titles¶
In [65]:
# average Word2Vec
# compute average word2vec for each review.
train_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_train['clean_project_title']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_title_tfidf_w2v_vectors.append(vector)
print(len(train_title_tfidf_w2v_vectors))
print(len(train_title_tfidf_w2v_vectors[0]))
In [66]:
# average Word2Vec
# compute average word2vec for each review.
test_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(x_test['clean_project_title']): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_title_tfidf_w2v_vectors.append(vector)
print(len(test_title_tfidf_w2v_vectors))
print(len(test_title_tfidf_w2v_vectors[0]))
1.5.3 Vectorizing Numerical features¶
In [67]:
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
project_data = pd.merge(project_data, price_data, on='id', how='left')
print(price_data.head())
#print(project_data.columns)
print(x_train.columns)
In [68]:
x_train = pd.merge(x_train, price_data, on = "id", how = "left")
x_test = pd.merge(x_test, price_data, on = "id", how = "left")
Standardize Price¶
In [69]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
price_scalar = StandardScaler()
price_scalar.fit(x_train['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_price_standar = price_scalar.transform(x_train['price'].values.reshape(-1, 1))
price_scalar.fit(x_test['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_price_standar = price_scalar.transform(x_test['price'].values.reshape(-1, 1))
In [70]:
print(train_price_standar.shape, y_train.shape)
print(test_price_standar.shape, y_test.shape)
Standardize Teacher previously posted Projects¶
In [71]:
warnings.filterwarnings("ignore")
price_scalar.fit(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_prev_proj_standar = price_scalar.transform(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
price_scalar.fit(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_prev_proj_standar = price_scalar.transform(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
In [72]:
print(train_prev_proj_standar.shape, y_train.shape)
print(test_prev_proj_standar.shape, y_test.shape)
Standardize Quantity¶
In [73]:
price_scalar.fit(x_train['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_quantity_standar = price_scalar.transform(x_train['quantity'].values.reshape(-1, 1))
price_scalar.fit(x_test['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_quantity_standar = price_scalar.transform(x_test['quantity'].values.reshape(-1, 1))
In [74]:
print(train_quantity_standar.shape, y_train.shape)
print(test_quantity_standar.shape, y_test.shape)
Standardize Title_word_count¶
In [75]:
title_scalar = StandardScaler()
title_scalar.fit(x_train['title_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {title_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
train_title_word_count_standar = title_scalar.transform(x_train['title_word_count'].values.reshape(-1, 1))
title_scalar.fit(x_test['title_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {title_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
test_title_word_count_standar = title_scalar.transform(x_test['title_word_count'].values.reshape(-1, 1))
print(train_title_word_count_standar.shape, y_train.shape)
print(test_title_word_count_standar.shape, y_test.shape)
Standardize Essay_word_count¶
In [76]:
essay_scalar = StandardScaler()
essay_scalar.fit(x_train['essay_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_essay_word_count_standar = essay_scalar.transform(x_train['essay_word_count'].values.reshape(-1, 1))
essay_scalar.fit(x_train['essay_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_essay_word_count_standar = essay_scalar.transform(x_test['essay_word_count'].values.reshape(-1, 1))
print(train_essay_word_count_standar.shape, y_train.shape)
print(test_essay_word_count_standar.shape, y_test.shape)
Standardize Positive Intensity¶
In [77]:
essay_scalar.fit(x_train['positive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_positive_standar = essay_scalar.transform(x_train['positive'].values.reshape(-1, 1))
essay_scalar.fit(x_train['positive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_positive_standar = essay_scalar.transform(x_test['positive'].values.reshape(-1, 1))
print(train_positive_standar.shape, y_train.shape)
print(test_positive_standar.shape, y_test.shape)
Standarsize Negitive Intensity¶
In [78]:
essay_scalar.fit(x_train['negitive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_negitive_standar = essay_scalar.transform(x_train['negitive'].values.reshape(-1, 1))
essay_scalar.fit(x_train['negitive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_negitive_standar = essay_scalar.transform(x_test['negitive'].values.reshape(-1, 1))
print(train_negitive_standar.shape, y_train.shape)
print(test_negitive_standar.shape, y_test.shape)
Standardize Neutral Intensity¶
In [79]:
essay_scalar.fit(x_train['neutral'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_neutral_standar = essay_scalar.transform(x_train['neutral'].values.reshape(-1, 1))
essay_scalar.fit(x_train['neutral'].values.reshape(-1,1))
test_neutral_standar = essay_scalar.transform(x_test['neutral'].values.reshape(-1, 1))
print(train_neutral_standar.shape, y_train.shape)
print(test_neutral_standar.shape, y_test.shape)
Assignment 9: RF & GBDT¶
The response tabel is built only on train dataset. For a category which is not there in train data and present in test data, we will encode them with default values Ex: in our test data if have State: D then we encode it as [0.5, 0.05]
- Apply both Random Forrest and GBDT on these feature sets
- Set 1: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(BOW) + preprocessed_eassay (BOW)
- Set 2: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF)
- Set 3: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V)
- Set 4: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF W2V)+ preprocessed_eassay (TFIDF W2V)
- The hyper paramter tuning (Consider any two hyper parameters preferably n_estimators, max_depth)
- Find the best hyper parameter which will give the maximum AUC value
- find the best hyper paramter using k-fold cross validation/simple cross validation data
- use gridsearch cv or randomsearch cv or you can write your own for loops to do this task
-
Representation of results
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure
with X-axis as n_estimators, Y-axis as max_depth, and Z-axis as AUC Score , we have given the notebook which explains how to plot this 3d plot, you can find it in the same drive 3d_scatter_plot.ipynb - You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure
seaborn heat maps with rows as n_estimators, columns as max_depth, and values inside the cell representing AUC Score - You can choose either of the plotting techniques: 3d plot or heat map
- Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.
- Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points
or
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure
- Conclusion
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
Note: Data Leakage
- There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.
- To avoid the issue of data-leakage, make sure to split your data first and then vectorize it.
- While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.
- For more details please go through this link.
Random Forest and GBDT¶
2.1 RF and GBDT Set-1¶
Merging all Categorical and Numerical _ SET-1 BOW Encoding¶
In [80]:
print(X_train_clean_cat_ohe.shape)
print(X_train_clean_subcat_ohe.shape)
print(X_train_grade_ohe.shape)
print(X_train_state_ohe.shape)
print(X_train_teacher_ohe.shape)
print(train_text_bow.shape)
print(test_text_bow.shape)
In [81]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train1 = hstack((X_train_clean_cat_ohe, X_train_clean_subcat_ohe, X_train_grade_ohe, X_train_state_ohe, X_train_teacher_ohe, train_text_bow
,train_title_bow)).tocsr()
X_test1 = hstack((X_test_clean_cat_ohe, X_test_clean_subcat_ohe, X_test_grade_ohe, X_test_state_ohe, X_test_teacher_ohe, test_text_bow
,test_title_bow)).tocsr()
print(X_train1.shape, y_train.shape)
print(X_test1.shape, y_test.shape)
print(type(X_train1))
# train_text_tfidf, train_title_tfidf, train_avg_w2v_vectors, train_title_avg_w2v_vectors,train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors
#test_text_tfidf, test_title_tfidf, test_avg_w2v_vectors, test_title_avg_w2v_vectors,test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors
In [82]:
print(X_train1.shape, y_train.shape)
Hyperparameter Tunning GridSearch¶
In [90]:
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
import seaborn as sea
In [91]:
RF = RandomForestClassifier()
parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}
classifier = GridSearchCV(RF, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train1, y_train)
Out[91]:
In [92]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
From depth 100 and sample split 100, we are gtting accuracy greater than 90
In [93]:
#################################PLOTly Plot##############################
#x1 = [1, 5, 10, 50, 100, 500]
#y1 = [5, 10, 100, 500]
#z1 = [0.5475, 0.6478, 0.702, 0.9442]
# https://plot.ly/python/3d-axes/
#trace1 = go.Scatter3d(x=x1,y=y1,z=z1, name = 'train')
#trace2 = go.Scatter3d(x=x2,y=y2,z=z2, name = 'Cross validation')
#data = [trace1]
#layout = go.Layout(scene = dict(
# xaxis = dict(title='max_depth'),
# yaxis = dict(title='min_samples_split'),
# zaxis = dict(title='AUC'),))
#
#fig = go.Figure(data=data, layout=layout)
#offline.iplot(fig, filename='3d-scatter-colorscale')
Trainng Model Using Best HyperParameter¶
In [94]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [121]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(max_depth = 10, min_samples_split = 250)
classifier.fit(X_train1, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train1)
y_test_pred = batch_predict(classifier,X_test1)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matrix for Train and Test Data¶
In [97]:
def predict(proba, threshould, fpr, tpr):
t = threshould[np.argmax(fpr*(1-tpr))]
print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
predictions = []
global predictions1
for i in proba:
if i>=t:
predictions.append(1)
else:
predictions.append(0)
predictions1 = predictions
return predictions
In [98]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.2 Merging all Categorical and Numerical _ SET-2 TF-IDF Encoding¶
In [83]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train2 = hstack((X_train_clean_cat_ohe, X_train_clean_subcat_ohe, X_train_grade_ohe, X_train_state_ohe, X_train_teacher_ohe,
train_text_tfidf, train_title_tfidf)).tocsr()
X_test2 = hstack((X_test_clean_cat_ohe, X_test_clean_subcat_ohe, X_test_grade_ohe, X_test_state_ohe, X_test_teacher_ohe,
test_text_tfidf, test_title_tfidf)).tocsr()
print(X_train2.shape, y_train.shape)
print(X_test2.shape, y_test.shape)
print(type(X_train2))
# train_text_tfidf, train_title_tfidf, train_avg_w2v_vectors, train_title_avg_w2v_vectors,train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors
#test_text_tfidf, test_title_tfidf, test_avg_w2v_vectors, test_title_avg_w2v_vectors,test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors
Hyperparameter Tunning¶
In [84]:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
RF = RandomForestClassifier()
parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}
classifier = GridSearchCV(RF, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train2, y_train)
Out[84]:
In [85]:
import seaborn as sea
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Model using best Hyperparameter¶
In [112]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(max_depth = 10, min_samples_split = 250)
classifier.fit(X_train2, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train2)
y_test_pred = batch_predict(classifier,X_test2)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matrix For Train & Test Data¶
In [101]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.3 Merging all Categorical and Numerical _ SET-3 AVG-W2V Encoding¶
In [84]:
X_train3 = hstack((X_train_clean_cat_ohe, X_train_clean_subcat_ohe, X_train_grade_ohe, X_train_state_ohe, X_train_teacher_ohe,
train_avg_w2v_vectors, train_title_avg_w2v_vectors)).tocsr()
X_test3 = hstack((X_test_clean_cat_ohe, X_test_clean_subcat_ohe, X_test_grade_ohe, X_test_state_ohe, X_test_teacher_ohe,
test_avg_w2v_vectors, test_title_avg_w2v_vectors)).tocsr()
print(X_train3.shape, y_train.shape)
print(X_test3.shape, y_test.shape)
print(type(X_train3))
# train_text_tfidf, train_title_tfidf, train_avg_w2v_vectors, train_title_avg_w2v_vectors,train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors
#test_text_tfidf, test_title_tfidf, test_avg_w2v_vectors, test_title_avg_w2v_vectors,test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors
In [85]:
print(X_train3.shape, y_train.shape)
Hyperparameter Tunning¶
In [119]:
RF = RandomForestClassifier()
parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}
classifier = GridSearchCV(RF, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train3, y_train)
Out[119]:
In [120]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Model Using Hyperparameter¶
In [113]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(max_depth = 5, min_samples_split = 500)
classifier.fit(X_train3, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train3)
y_test_pred = batch_predict(classifier,X_test3)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Train & Test Confusion Matrix¶
In [114]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.4 Merging all Categorical and Numerical _ SET-4 TFIDF-W2V Encoding¶
In [86]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train4 = hstack((X_train_clean_cat_ohe, X_train_clean_subcat_ohe, X_train_grade_ohe, X_train_state_ohe, X_train_teacher_ohe,
train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors)).tocsr()
X_test4 = hstack((X_test_clean_cat_ohe, X_test_clean_subcat_ohe, X_test_grade_ohe, X_test_state_ohe, X_test_teacher_ohe,
test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors)).tocsr()
print(X_train4.shape, y_train.shape)
print(X_test4.shape, y_test.shape)
print(type(X_train2))
# train_text_tfidf, train_title_tfidf, train_avg_w2v_vectors, train_title_avg_w2v_vectors,train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors
#test_text_tfidf, test_title_tfidf, test_avg_w2v_vectors, test_title_avg_w2v_vectors,test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors
In [87]:
print(X_train4.shape, y_train.shape)
Hyperparameter Tunning¶
In [128]:
RF = RandomForestClassifier()
parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500, 1000, 1250]}
classifier = GridSearchCV(RF, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train4, y_train)
Out[128]:
In [129]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Model using Best Hyperparameter¶
In [118]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(max_depth = 5, min_samples_split = 500)
classifier.fit(X_train4, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train4)
y_test_pred = batch_predict(classifier,X_test4)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Train & Test Confusion Matrix¶
In [119]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.5 Apply Gradient Boosted Decision Trees (GBDT)¶
Applying Gradient Boosted Decision Tree on same sets again to verify which algo is performing better, we need to do hyperparameter tuning corresponding to the model you selected and procedure in step 2 and step 3 </br>
2.5.1 Applying GBDT on Set - 1¶
In [88]:
print(X_train1.shape, y_train.shape)
Hyperparameter Tunning using GridSearchCV¶
In [96]:
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier
In [139]:
GBDT = GradientBoostingClassifier()
parameters = {'max_depth' : [5, 10, 20, 50, 100], 'n_estimators' : [10, 25, 50, 100, 250]}
classifier = GridSearchCV(GBDT, parameters, cv=3, scoring = 'roc_auc')
classifier.fit(X_train1, y_train)
Out[139]:
In [143]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_n_estimators', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Using Best Hyperparameter¶
In [123]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier(max_depth = 5 , min_samples_split = 250)
classifier.fit(X_train1, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train1)
y_test_pred = batch_predict(classifier,X_test1)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matrix¶
In [124]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.5.2 Applying GBDT on Set - 2¶
In [89]:
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
print(X_train2.shape, y_train.shape)
HyperParameter Tunning¶
In [85]:
GBDT = GradientBoostingClassifier()
parameters = {'max_depth' : [5, 10, 20, 50, 100], 'n_estimators' : [10, 25, 50, 100, 250]}
classifier = GridSearchCV(GBDT, parameters, cv=3, scoring = 'roc_auc')
classifier.fit(X_train2, y_train)
Out[85]:
In [86]:
import seaborn as sea
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_n_estimators', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Using Best Hyperparameters¶
In [127]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier(max_depth = 10 , min_samples_split = 250)
classifier.fit(X_train2, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train2)
y_test_pred = batch_predict(classifier,X_test2)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Train and Test Confusion Matrix¶
In [128]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.5.3 Applying GBDT on Set - 3¶
In [90]:
print(X_train3.shape, y_train.shape)
HyperParameter Tunning¶
In [87]:
GBDT = GradientBoostingClassifier()
parameters = {'max_depth' : [5, 10, 20, 50, 100], 'n_estimators' : [10, 25, 50, 100, 250]}
classifier = GridSearchCV(GBDT, parameters, cv=3, scoring = 'roc_auc')
classifier.fit(X_train3, y_train)
Out[87]:
In [89]:
import seaborn as sea
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_n_estimators', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Using Best Hyperparameter¶
In [131]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier(max_depth = 5 , min_samples_split = 50)
classifier.fit(X_train3, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train3)
y_test_pred = batch_predict(classifier,X_test3)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Train and Test Confusion Matrix¶
In [132]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
2.5.4 Applying GBDT on Set - 4¶
In [91]:
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
print(X_train4.shape, y_train.shape)
HyperParameter Tunning¶
In [90]:
GBDT = GradientBoostingClassifier()
parameters = {'max_depth' : [5, 10, 20, 50, 100], 'n_estimators' : [10, 25, 50, 100, 250]}
classifier = GridSearchCV(GBDT, parameters, cv=3, scoring = 'roc_auc')
classifier.fit(X_train4, y_train)
Out[90]:
In [91]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_n_estimators', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]
fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('CV Set')
plt.show()
Training Using Best Hyperparameter¶
In [135]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier(max_depth = 5 , min_samples_split = 100)
classifier.fit(X_train4, y_train)
#classifier_viz.fit(X_train1, y_train)
y_train_pred = batch_predict(classifier,X_train4)
y_test_pred = batch_predict(classifier,X_test4)
#y_test_pred = batch_predict(classifier_viz,X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Train and Test Confusion Matrix¶
In [137]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns
sns.set()
con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))
key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5))
labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)
sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])
ax[0].set_title('Train Set')
ax[1].set_title('Test Set')
plt.show()
3. Conclusion
In [120]:
# Please compare all your models using Prettytable library
# http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
TB = PrettyTable()
TB.field_names = ["Rand_Forest - MODEL", "HyperparameterS", "Train_AUC", "Test_Auc"]
TB.title = "Decision Tree"
TB.add_row(["BOW-ENC-RF", "Depth:10 | Samp_Split:250", 0.67,0.63])
TB.add_row(["TFIDF-ENC-RF", "Depth:10 | Samp_Split:250", 0.66, 0.62])
TB.add_row(["AvgW2V-ENC-RF", "Depth:5 | Samp_Split:500", 0.64, 0.60])
TB.add_row(["Tf-Idf-ENC-RF", "Depth:5 | Samp_Split:250", 0.65, 0.61])
print(TB)
TB1 = PrettyTable()
TB1.field_names = ["GBDT - MODEL", "HyperparameterS", "Train_AUC", "Test_Auc"]
TB1.title = "Gradient Boosting Decision Tree"
TB1.add_row(["BOW-ENC-GBDT", "Depth:5 | Samp_Split:250", 0.94,0.71])
TB1.add_row(["TFIDF-ENC-GBDT", "Depth:10 | Samp_Split:250", 0.92, 0.68])
TB1.add_row(["AvgW2V-ENC-GBDT", "Depth:5 | Samp_Split:50", 0.88, 0.67])
TB1.add_row(["Tf-Idf-ENC-GBDT", "Depth:5 | Samp_Split:100", 0.88, 0.69])
print(TB1)
Observations: </br>